Two-area Photovoltaic Automatic Generation Control Based On Improved Grey Wolf Algorithm

The global energy crisis and environmental pollution problems have led people to turn their attention to renewable energy.Solar energy is favored for its inexhaustible and widely distributed features.A large number of photovoltaic grids will bring more load disturbance to the power system,causing fluctuations in the system frequency.The randomness and uncertainty of new energy sources are also constantly impacting the frequency stability of the interconnected power grid.Automatic Generation Control(AGC)is the main control method for regulating the power and frequency deviation of modern power grids,which affects the power quality and grid stability of interconnected power grids.In this paper,for the automatic power generation control of photovoltaic grid-connected,the two-area interconnected automatic power generation control system is regulated by load frequency control.In depth analysis of mathematical models,control modes,and performance evaluation criteria for automatic power generation control in two areas.Based on the mathematical model to build the simulation model for the two-area interconnected load frequency control system model in Matlab/Simulink software.Secondly,the PI/PID controller with filter coefficient is selected as the controller of the two-area interconnected automatic power generation control model.In view of the lack of adjustment precision and adjustment time of traditional PI/PID controllers,the Grey Wolf Optimization(GWO)algorithm is used to optimize the PI/PID control parameters.Aiming at the shortage of grey wolf algorithm which is easy to fall into local optimum and optimization speed,an improved convergence factor and dynamic weighting strategy are proposed to improve it.In this paper,the traditional PI control,genetic algorithm(GA)and particle swarm optimization(PSO),gray wolf optimization algorithm and improved gray wolf optimization algorithm(CGWO)respectively control the PI/PID control of the disturbed two-area interconnected automatic power generation control system model.The device performs parameter tuning.The comparison of optimization results shows that the improved gray wolf optimization algorithm has obvious advantages in terms of adjustment accuracy,adjustment time and robustness.Finally,this paper builds a photovoltaic array working condition model,and connects the photovoltaic power generation system with maximum power point tracking control into the two-area interconnected automatic power generation control system.The real data of a photovoltaic power station in Hebei was collected to simulate the photovoltaic power generation system to achieve the real simulation of photovoltaic power generation.The photovoltaic power generation access automatic power generation control system brings more load disturbance to it.Using the improved gray wolf algorithm to optimize the tuning controller parameters to adjust the system to verify the improved gray wolf optimization algorithm against large disturbances.The optimization results of the improved gray wolf optimization algorithm are compared with particle swarm optimization algorithm,genetic algorithm and gray wolf algorithm.The experimental results show that in the face of the random load disturbance caused by the photovoltaic power generation system accessing the power grid,the genetic algorithm and the particle swarm algorithm all show the defects of slow optimization speed and low precision of optimization,but the improved gray wolf optimization algorithm still maintains excellent control performance and can effectively suppress the power disturbance caused by the photovoltaic power generation system connecting to the power grid.